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You cannot protect proprietary code with a single “do not train” switch. Configure four separate boundaries: whether prompts and outputs may improve models, how long content and logs are retained, what telemetry your editor sends, and which files, commands, credentials, network destinations, and cloud environments the assistant can reach. The correct settings depend on the provider, account or plan, IDE, agent harness, and repository owner.
What you must control before sharing private code
“Private” can mean several different risks. A provider may be prohibited from using your code to improve a model while still retaining it for abuse monitoring. Your editor may send usage telemetry even when model training is disabled. An agent may also read files, execute commands, or upload context to a cloud sandbox according to its task permissions. Treat each concern as a separate configuration question.
| Control | Question to answer | Why it is separate |
|---|---|---|
| Model training or improvement | Can new conversations, prompts, code, or outputs be used to improve models? | An opt-out governs model-improvement use, not necessarily storage, monitoring, or access. |
| Retention and logging | How long are prompts, responses, derived metadata, and abuse-monitoring records stored? | Content can be retained even when it is excluded from training. Endpoint-specific state may have different terms. |
| Telemetry | What does the IDE or extension report about usage, errors, suggestions, or files? | Editor telemetry is a client setting and may not follow the provider’s training preference. |
| Agent access | Which paths, commands, credentials, network destinations, and cloud environments can the agent use? | An assistant can expose code during execution or retrieval even if the provider does not train on it. |
| Organization enforcement | Which administrator policy overrides a user preference, and which clients does it cover? | Personal settings, device policies, enterprise-managed settings, and service policies have different scopes. |
Do not promise that code “never leaves your machine” unless you have verified the selected product’s execution model, network traffic, and logging behavior for the exact account and client.
Start with authorization and an inventory
Use the account your employer approved
For a work repository, use the organization-approved account, assistant configuration, and endpoint. Do not paste proprietary code into a personal account or an unapproved provider merely because its default appears convenient. Ask your security or IT owner which plans, clients, regions, and integrations are permitted. This is a governance decision, not a judgment that a particular service is universally safe or unsafe.
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Record the complete setup
Write down the provider, product name, account owner, plan, IDE or command-line client, agent harness, repository owner, and whether execution is local, cloud-based, or API-mediated. A policy that applies to a business account may not apply to an individual subscription; a setting in an IDE workspace may not apply to a cloud agent. Capture the date of your review because provider policies change.
Step-by-step setup for a private repository
1. Select the account and inspect data-use preferences
- Sign in with the approved account before opening the private repository.
- Open the provider’s current privacy, data-use, training, and model-improvement controls. Confirm whether the preference applies to new conversations, coding tasks, uploaded files, feedback, or only a particular product.
- For individual OpenAI ChatGPT or Codex use, turn off
Improve the model for everyoneor selectDo not train on my content, as available in the current account interface. This applies to new conversations and tasks; it is not a deletion or zero-retention promise. - In Codex, review the separate
Include environmentscontrol. It governs whether environment information is included with a task and is independent of the model-improvement preference. - If you use GitHub Copilot as an individual subscriber, review the individual training-data policy and its personal opt-out setting. GitHub documents a policy for certain individual plans beginning April 24, 2026, so verify the current wording and effective date in your account before relying on it.
OpenAI’s Help Center states: “By default, we don’t use inputs or outputs from ChatGPT Business, ChatGPT Enterprise, ChatGPT Edu, or our API to improve our models.” That statement is about model improvement. It does not mean that content is never retained, logged, or accessible to a configured agent. OpenAI also notes that API organization owners can enable data sharing, so check the organization-level choice rather than assuming the default.
2. Review IDE telemetry and built-in AI features
In Visual Studio Code, follow the Copilot setup flow and confirm which account is shown in the Copilot interface. The setup documentation describes free-tier telemetry as enabled and identifies separate controls for VS Code telemetry, Copilot settings, and built-in AI features.
- Open Settings and search for
telemetry.telemetryLevel. Set it tooffif your organization requires VS Code telemetry to be disabled. - Open Copilot Settings and review telemetry and code-suggestion options. Check whether the setting is user, workspace, or device scoped.
- If you must disable built-in AI features, set
chat.disableAIFeaturesat the required scope. A workspace setting affects that workspace; it is not an organization-wide enforcement mechanism. - Reload the window and verify that the effective values remain after reopening the repository.
Telemetry controls do not revoke an agent’s file permissions and do not alter provider retention terms. Conversely, an organization policy may override a local preference, so record the effective value rather than only the value you attempted to set.
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3. Restrict files, commands, network, and credentials
Give the agent the smallest useful working set. Start with a disposable branch or a sanitized fixture, then expand access only when the task requires it.
- Allow read access only to the repository and configuration files needed for the task. Exclude unrelated repositories, home-directory files, cloud metadata, and secrets stores.
- Block access to
.envfiles, private keys, credential helpers, production configuration, and password-manager exports unless a documented task requires a specific secret. - Limit write access to a temporary worktree or branch. Require a human review before merges, releases, database changes, dependency upgrades, or destructive commands.
- Restrict outbound network access to approved package registries and source-control endpoints. Deny arbitrary uploads and shell commands that transmit file contents.
- Use short-lived, least-privilege tokens. Prefer a token scoped to one repository and operation over a personal access token with broad organization rights.
GitHub documents local sandboxing for Copilot CLI and VS Code and policy controls for cloud sandbox access. Enable the narrowest sandbox available for your client, then test which paths and commands are actually reachable. A cloud sandbox policy is not the same as a local filesystem boundary.
An OpenAI article describing how it runs Codex safely discusses sandboxing, approval boundaries, credential controls, and agent-aware logs. That article is an example of governance controls used in an internal environment, not a universal default configuration or a guarantee about every Codex deployment.
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4. Apply team and enterprise policy at the correct layer
When multiple developers use an assistant, enforce controls instead of relying on a written recommendation.
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- Identify whether the requirement belongs in an enterprise-managed assistant setting, a managed-device policy, an IDE policy, a GitHub organization or enterprise setting, or a provider account setting.
- Check supported-client coverage. A policy that governs one Copilot client may not govern a different IDE, command-line tool, web interface, or API integration.
- In VS Code, run the Command Palette command
Developer: Policy Diagnostics. Inspect the effective value, source, and precedence for each relevant policy. - Document which administrator can change the policy and how exceptions are approved.
VS Code distinguishes enterprise-managed settings for supported Copilot clients from device policies for managed installations. GitHub organization and enterprise controls govern account-level and service-side behavior. A workspace setting is therefore not equivalent to an organization-enforced rule, and an enterprise policy can override a user preference.
5. Verify retention and logging independently
Read the terms for the exact endpoint, plan, and feature you are using. For the OpenAI API, the data-controls documentation says abuse-monitoring logs may contain prompts, responses, and derived metadata and are retained for up to 30 days by default. Longer retention may apply when legally required or reasonably necessary to protect the service or other people.
Eligible API customers can apply for Modified Abuse Monitoring or Zero Data Retention. Approval is required, eligibility is limited, and some endpoints or features can still store state. Do not describe either option as a blanket deletion guarantee; verify which endpoints, files, fine-tuning jobs, responses, and application logs are covered.
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6. Run a harmless verification task
- Confirm the signed-in identity and repository owner.
- Use a non-sensitive prompt that reveals which assistant and model are active without including proprietary code.
- Check the assistant’s reported permissions by asking it to list an allowed test directory, not your home directory or secrets.
- Run
Developer: Policy Diagnosticsin VS Code when applicable and save the effective policy output according to your organization’s process. - Confirm the sandbox blocks a deliberately disallowed path or command.
- Ask the administrator to verify account-level training and retention settings when you cannot inspect them yourself.
This test establishes which account and policy are active; it does not prove that every provider-side control is effective. Treat administrator confirmation and the provider’s current terms as authoritative for retention and model-improvement behavior.
Compare deployment choices by control, not by brand
There is no universal safest assistant. Compare the implementation you are considering against the same dimensions.
| Decision axis | Questions to document | Evidence to request |
|---|---|---|
| Account and plan | Is it personal, business, enterprise, or API-based? Who owns the account? | Current plan terms, administrator ownership, and effective account setting. |
| Training opt-out | Does it cover prompts, outputs, code suggestions, feedback, and tasks? Does it apply only to new data? | Provider policy and a screenshot or export of the active preference. |
| Retention and logs | What is stored, for how long, and by which endpoint or feature? | Data-controls documentation and any approved retention exception. |
| Execution location | Does inference or agent execution occur locally, in a cloud sandbox, or through an API? | Architecture description, network routes, and sandbox policy. |
| Organization enforcement | Can an administrator force the setting, and which clients are covered? | Policy scope, precedence rules, and effective diagnostics. |
| Credentials and approvals | Can the agent read tokens or execute consequential commands without review? | Permission model, approval prompts, token scope, and audit records. |
| Verification | Can you see the effective value rather than merely choose a preference? | Policy diagnostics, administrator reports, and reproducible checks. |
A local editor can still send prompts to a hosted model. A cloud agent can still be constrained by a narrow sandbox. Judge the actual data path and permissions, not the marketing label attached to the deployment.
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“I disabled training, so retention no longer matters.”
Why it fails: model-improvement use and storage are different controls. Fix: review retention, abuse-monitoring logs, conversation history, endpoint state, and deletion procedures separately.
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“The workspace setting protects every developer.”
Why it fails: workspace settings affect one project and can be overridden or bypassed by another client. Fix: use organization or enterprise policy for required behavior, confirm supported clients, and inspect effective values with policy diagnostics.
“The agent only sees the file I opened.”
Why it fails: an agent may search the workspace, invoke tools, read environment variables, or reach network resources permitted by its sandbox. Fix: inspect filesystem and network permissions, remove secrets from the environment, and test denied paths.
“Business defaults mean nothing can be logged.”
Why it fails: a no-training default addresses model improvement, while service security and abuse monitoring can still create logs. Fix: read the retention terms for the exact account, endpoint, and feature.
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Why it fails: policies can have precedence rules and limited client coverage. Fix: inspect the effective setting on a managed device and keep the diagnostics output or administrator confirmation.
“An internal safety article is a deployment recipe.”
Why it fails: internal controls may depend on infrastructure unavailable to customers. Fix: treat internal examples as design principles, then verify the controls exposed by your own plan and client.
A repeatable privacy checklist
- Authorization: The repository owner approved the account, assistant, plan, and client.
- Identity: The signed-in account is the intended personal, business, enterprise, or API identity.
- Training: The current model-improvement preference covers the data types and tasks you will send.
- Retention: You know the storage and logging terms for the exact endpoint and feature.
- Telemetry: IDE telemetry, Copilot telemetry, and code-suggestion settings match policy.
- Access: Filesystem, network, shell, and credential permissions are limited to the task.
- Approvals: Human confirmation is required for writes, secrets, external uploads, and consequential commands.
- Enforcement: Organization or enterprise policy is applied at the correct layer and covers every supported client in use.
- Verification: A harmless test, policy diagnostics, or administrator check confirms the effective configuration.
- Change control: Recheck settings after provider policy changes, plan changes, IDE upgrades, or switching from an IDE assistant to an API or cloud agent.
How to keep the setup accurate over time
Provider policies, account defaults, and client capabilities change. GitHub’s individual Copilot policy described above is dated to take effect April 24, 2026; that date is not a permanent guarantee for later plans. Schedule a review whenever a developer changes account type, adds a new client, enables a cloud agent, grants a new token, or moves a repository between organizations.
Keep a short control record containing the account and plan, policy screenshots or diagnostics, approved clients, retention terms, sandbox configuration, token scope, and review date. Remove sensitive code from prompts whenever a smaller example, synthetic fixture, or local test can answer the question. Privacy is strongest when the assistant receives less proprietary material and has fewer ways to act on it.
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